In the evolving landscape of recommender systems, Graph Contrastive Learning (GCL) has become a prominent method for enhancing recommendation performance by alleviating the issue of data sparsity. However, existing GCL-based recommendations often overlook the control of shared information between the contrastive views. In this paper, we initially analyze and experimentally demonstrate these methods often lead to the issue of augmented representation collapse, where the representations between views become excessively similar, diminishing their distinctiveness. To address this issue, we propose the Information-Controllable Graph Contrastive Learning (IGCL) framework, a novel approach that focuses on optimizing the shared information between views to include as much relevant information for the recommendation task as possible while maintaining an appropriate level. In particular, we design the Collaborative Signals Enhanced Augmentation module to infuse the augmented representation with rich, task-relevant collaborative signals. Furthermore, the Information-Controllable Contrastive Learning module is designed to direct control over the magnitude of shared information between the contrastive views to avoid over-similarity. Extensive experiments on three public datasets demonstrate the effectiveness of IGCL, showcasing significant improvements in performance and the capability to alleviate augmented representation collapse.

Aspect-based sentiment analysis is a popular task in Natural Language Processing (NLP). Many methods utilize attention mechanisms and graph neural networks on dependency trees to identify the most relevant opinion words for aspects. However, improvements are limited due to the presence of rare words in small datasets and the inability to guarantee that each token will have a high probability of interacting with aspects. To address these challenges, we propose a Prior Knowledge Augmentation Network (PKAN) model that incorporates rich semantic information from knowledge graphs, syntactic structures, and prior probabilistic position embeddings. Specifically, to better comprehend semantic information, we design a knowledge augmentation module that constructs an adjacency matrix based on knowledge graphs, dependency trees and dataset co-occurrences. Additionally, to capture opinion words more comprehensively, we propose a probabilistic relative position embedding strategy. This strategy assumes that the semantic distribution of tokens at varying distances from the central word obeys a gaussian distribution and constrains the distance between words and aspects within one standard deviation. Experimental results on five public datasets validate the effectiveness of our model.